Overview

Brought to you by YData

Dataset statistics

Number of variables25
Number of observations103904
Missing cells310
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory19.8 MiB
Average record size in memory200.0 B

Variable types

Numeric19
Categorical6

Alerts

Arrival Delay in Minutes is highly overall correlated with Departure Delay in MinutesHigh correlation
Class is highly overall correlated with Type of Travel and 1 other fieldsHigh correlation
Cleanliness is highly overall correlated with Food and drink and 2 other fieldsHigh correlation
Departure Delay in Minutes is highly overall correlated with Arrival Delay in MinutesHigh correlation
Ease of Online booking is highly overall correlated with Inflight wifi serviceHigh correlation
Food and drink is highly overall correlated with Cleanliness and 2 other fieldsHigh correlation
Inflight entertainment is highly overall correlated with Cleanliness and 2 other fieldsHigh correlation
Inflight service is highly overall correlated with On-board serviceHigh correlation
Inflight wifi service is highly overall correlated with Ease of Online booking and 1 other fieldsHigh correlation
On-board service is highly overall correlated with Inflight serviceHigh correlation
Online boarding is highly overall correlated with satisfactionHigh correlation
Seat comfort is highly overall correlated with Cleanliness and 2 other fieldsHigh correlation
Type of Travel is highly overall correlated with ClassHigh correlation
satisfaction is highly overall correlated with Class and 2 other fieldsHigh correlation
Unnamed: 0 is uniformly distributedUniform
id is uniformly distributedUniform
Unnamed: 0 has unique valuesUnique
id has unique valuesUnique
Inflight wifi service has 3103 (3.0%) zerosZeros
Departure/Arrival time convenient has 5300 (5.1%) zerosZeros
Ease of Online booking has 4487 (4.3%) zerosZeros
Online boarding has 2428 (2.3%) zerosZeros
Departure Delay in Minutes has 58668 (56.5%) zerosZeros
Arrival Delay in Minutes has 58159 (56.0%) zerosZeros

Reproduction

Analysis started2024-10-17 06:41:29.642207
Analysis finished2024-10-17 06:42:11.225112
Duration41.58 seconds
Software versionydata-profiling vv4.10.0
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct103904
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51951.5
Minimum0
Maximum103903
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size811.9 KiB
2024-10-17T15:42:11.335087image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5195.15
Q125975.75
median51951.5
Q377927.25
95-th percentile98707.85
Maximum103903
Range103903
Interquartile range (IQR)51951.5

Descriptive statistics

Standard deviation29994.646
Coefficient of variation (CV)0.5773586
Kurtosis-1.2
Mean51951.5
Median Absolute Deviation (MAD)25976
Skewness0
Sum5.3979687 × 109
Variance8.9967876 × 108
MonotonicityStrictly increasing
2024-10-17T15:42:11.465238image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
< 0.1%
69266 1
 
< 0.1%
69276 1
 
< 0.1%
69275 1
 
< 0.1%
69274 1
 
< 0.1%
69273 1
 
< 0.1%
69272 1
 
< 0.1%
69271 1
 
< 0.1%
69270 1
 
< 0.1%
69269 1
 
< 0.1%
Other values (103894) 103894
> 99.9%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
ValueCountFrequency (%)
103903 1
< 0.1%
103902 1
< 0.1%
103901 1
< 0.1%
103900 1
< 0.1%
103899 1
< 0.1%
103898 1
< 0.1%
103897 1
< 0.1%
103896 1
< 0.1%
103895 1
< 0.1%
103894 1
< 0.1%

id
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct103904
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64924.211
Minimum1
Maximum129880
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size811.9 KiB
2024-10-17T15:42:11.591929image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6593.15
Q132533.75
median64856.5
Q397368.25
95-th percentile123409.7
Maximum129880
Range129879
Interquartile range (IQR)64834.5

Descriptive statistics

Standard deviation37463.812
Coefficient of variation (CV)0.57703917
Kurtosis-1.1984401
Mean64924.211
Median Absolute Deviation (MAD)32410
Skewness0.0028642483
Sum6.7458852 × 109
Variance1.4035372 × 109
MonotonicityNot monotonic
2024-10-17T15:42:11.725853image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
70172 1
 
< 0.1%
116739 1
 
< 0.1%
6259 1
 
< 0.1%
17470 1
 
< 0.1%
118574 1
 
< 0.1%
23529 1
 
< 0.1%
16272 1
 
< 0.1%
58438 1
 
< 0.1%
2352 1
 
< 0.1%
65908 1
 
< 0.1%
Other values (103894) 103894
> 99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
129880 1
< 0.1%
129879 1
< 0.1%
129878 1
< 0.1%
129875 1
< 0.1%
129874 1
< 0.1%
129873 1
< 0.1%
129871 1
< 0.1%
129870 1
< 0.1%
129869 1
< 0.1%
129867 1
< 0.1%

Gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size811.9 KiB
Female
52727 
Male
51177 

Length

Max length6
Median length6
Mean length5.0149176
Min length4

Characters and Unicode

Total characters521070
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMale
2nd rowMale
3rd rowFemale
4th rowFemale
5th rowMale

Common Values

ValueCountFrequency (%)
Female 52727
50.7%
Male 51177
49.3%

Length

2024-10-17T15:42:11.855299image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-17T15:42:11.958029image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
female 52727
50.7%
male 51177
49.3%

Most occurring characters

ValueCountFrequency (%)
e 156631
30.1%
a 103904
19.9%
l 103904
19.9%
F 52727
 
10.1%
m 52727
 
10.1%
M 51177
 
9.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 521070
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 156631
30.1%
a 103904
19.9%
l 103904
19.9%
F 52727
 
10.1%
m 52727
 
10.1%
M 51177
 
9.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 521070
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 156631
30.1%
a 103904
19.9%
l 103904
19.9%
F 52727
 
10.1%
m 52727
 
10.1%
M 51177
 
9.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 521070
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 156631
30.1%
a 103904
19.9%
l 103904
19.9%
F 52727
 
10.1%
m 52727
 
10.1%
M 51177
 
9.8%

Customer Type
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size811.9 KiB
Loyal Customer
84923 
disloyal Customer
18981 

Length

Max length17
Median length14
Mean length14.548035
Min length14

Characters and Unicode

Total characters1511599
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLoyal Customer
2nd rowdisloyal Customer
3rd rowLoyal Customer
4th rowLoyal Customer
5th rowLoyal Customer

Common Values

ValueCountFrequency (%)
Loyal Customer 84923
81.7%
disloyal Customer 18981
 
18.3%

Length

2024-10-17T15:42:12.057003image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-17T15:42:12.147247image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
customer 103904
50.0%
loyal 84923
40.9%
disloyal 18981
 
9.1%

Most occurring characters

ValueCountFrequency (%)
o 207808
13.7%
l 122885
 
8.1%
s 122885
 
8.1%
y 103904
 
6.9%
a 103904
 
6.9%
103904
 
6.9%
C 103904
 
6.9%
u 103904
 
6.9%
t 103904
 
6.9%
m 103904
 
6.9%
Other values (5) 330693
21.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1511599
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 207808
13.7%
l 122885
 
8.1%
s 122885
 
8.1%
y 103904
 
6.9%
a 103904
 
6.9%
103904
 
6.9%
C 103904
 
6.9%
u 103904
 
6.9%
t 103904
 
6.9%
m 103904
 
6.9%
Other values (5) 330693
21.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1511599
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 207808
13.7%
l 122885
 
8.1%
s 122885
 
8.1%
y 103904
 
6.9%
a 103904
 
6.9%
103904
 
6.9%
C 103904
 
6.9%
u 103904
 
6.9%
t 103904
 
6.9%
m 103904
 
6.9%
Other values (5) 330693
21.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1511599
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 207808
13.7%
l 122885
 
8.1%
s 122885
 
8.1%
y 103904
 
6.9%
a 103904
 
6.9%
103904
 
6.9%
C 103904
 
6.9%
u 103904
 
6.9%
t 103904
 
6.9%
m 103904
 
6.9%
Other values (5) 330693
21.9%

Age
Real number (ℝ)

Distinct75
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.379706
Minimum7
Maximum85
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size811.9 KiB
2024-10-17T15:42:12.255636image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile14
Q127
median40
Q351
95-th percentile64
Maximum85
Range78
Interquartile range (IQR)24

Descriptive statistics

Standard deviation15.114964
Coefficient of variation (CV)0.38382622
Kurtosis-0.71956812
Mean39.379706
Median Absolute Deviation (MAD)12
Skewness-0.0045161271
Sum4091709
Variance228.46213
MonotonicityNot monotonic
2024-10-17T15:42:12.385664image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39 2969
 
2.9%
25 2798
 
2.7%
40 2574
 
2.5%
44 2482
 
2.4%
42 2457
 
2.4%
41 2456
 
2.4%
22 2351
 
2.3%
23 2346
 
2.3%
45 2339
 
2.3%
47 2329
 
2.2%
Other values (65) 78803
75.8%
ValueCountFrequency (%)
7 562
0.5%
8 640
0.6%
9 692
0.7%
10 683
0.7%
11 678
0.7%
12 635
0.6%
13 633
0.6%
14 707
0.7%
15 818
0.8%
16 899
0.9%
ValueCountFrequency (%)
85 17
 
< 0.1%
80 78
 
0.1%
79 42
 
< 0.1%
78 33
 
< 0.1%
77 87
0.1%
76 45
 
< 0.1%
75 61
 
0.1%
74 47
 
< 0.1%
73 51
 
< 0.1%
72 201
0.2%

Type of Travel
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size811.9 KiB
Business travel
71655 
Personal Travel
32249 

Length

Max length15
Median length15
Mean length15
Min length15

Characters and Unicode

Total characters1558560
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPersonal Travel
2nd rowBusiness travel
3rd rowBusiness travel
4th rowBusiness travel
5th rowBusiness travel

Common Values

ValueCountFrequency (%)
Business travel 71655
69.0%
Personal Travel 32249
31.0%

Length

2024-10-17T15:42:12.503572image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-17T15:42:12.702461image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
travel 103904
50.0%
business 71655
34.5%
personal 32249
 
15.5%

Most occurring characters

ValueCountFrequency (%)
s 247214
15.9%
e 207808
13.3%
r 136153
8.7%
a 136153
8.7%
l 136153
8.7%
n 103904
6.7%
103904
6.7%
v 103904
6.7%
B 71655
 
4.6%
u 71655
 
4.6%
Other values (5) 240057
15.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1558560
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 247214
15.9%
e 207808
13.3%
r 136153
8.7%
a 136153
8.7%
l 136153
8.7%
n 103904
6.7%
103904
6.7%
v 103904
6.7%
B 71655
 
4.6%
u 71655
 
4.6%
Other values (5) 240057
15.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1558560
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 247214
15.9%
e 207808
13.3%
r 136153
8.7%
a 136153
8.7%
l 136153
8.7%
n 103904
6.7%
103904
6.7%
v 103904
6.7%
B 71655
 
4.6%
u 71655
 
4.6%
Other values (5) 240057
15.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1558560
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 247214
15.9%
e 207808
13.3%
r 136153
8.7%
a 136153
8.7%
l 136153
8.7%
n 103904
6.7%
103904
6.7%
v 103904
6.7%
B 71655
 
4.6%
u 71655
 
4.6%
Other values (5) 240057
15.4%

Class
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size811.9 KiB
Business
49665 
Eco
46745 
Eco Plus
7494 

Length

Max length8
Median length8
Mean length5.7505678
Min length3

Characters and Unicode

Total characters597507
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEco Plus
2nd rowBusiness
3rd rowBusiness
4th rowBusiness
5th rowBusiness

Common Values

ValueCountFrequency (%)
Business 49665
47.8%
Eco 46745
45.0%
Eco Plus 7494
 
7.2%

Length

2024-10-17T15:42:12.800825image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-17T15:42:12.891102image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
eco 54239
48.7%
business 49665
44.6%
plus 7494
 
6.7%

Most occurring characters

ValueCountFrequency (%)
s 156489
26.2%
u 57159
 
9.6%
E 54239
 
9.1%
c 54239
 
9.1%
o 54239
 
9.1%
B 49665
 
8.3%
i 49665
 
8.3%
n 49665
 
8.3%
e 49665
 
8.3%
7494
 
1.3%
Other values (2) 14988
 
2.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 597507
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 156489
26.2%
u 57159
 
9.6%
E 54239
 
9.1%
c 54239
 
9.1%
o 54239
 
9.1%
B 49665
 
8.3%
i 49665
 
8.3%
n 49665
 
8.3%
e 49665
 
8.3%
7494
 
1.3%
Other values (2) 14988
 
2.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 597507
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 156489
26.2%
u 57159
 
9.6%
E 54239
 
9.1%
c 54239
 
9.1%
o 54239
 
9.1%
B 49665
 
8.3%
i 49665
 
8.3%
n 49665
 
8.3%
e 49665
 
8.3%
7494
 
1.3%
Other values (2) 14988
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 597507
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 156489
26.2%
u 57159
 
9.6%
E 54239
 
9.1%
c 54239
 
9.1%
o 54239
 
9.1%
B 49665
 
8.3%
i 49665
 
8.3%
n 49665
 
8.3%
e 49665
 
8.3%
7494
 
1.3%
Other values (2) 14988
 
2.5%

Flight Distance
Real number (ℝ)

Distinct3802
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1189.4484
Minimum31
Maximum4983
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size811.9 KiB
2024-10-17T15:42:12.997504image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum31
5-th percentile175
Q1414
median843
Q31743
95-th percentile3383
Maximum4983
Range4952
Interquartile range (IQR)1329

Descriptive statistics

Standard deviation997.14728
Coefficient of variation (CV)0.8383275
Kurtosis0.26853544
Mean1189.4484
Median Absolute Deviation (MAD)517
Skewness1.1094657
Sum1.2358844 × 108
Variance994302.7
MonotonicityNot monotonic
2024-10-17T15:42:13.121558image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
337 660
 
0.6%
594 395
 
0.4%
404 392
 
0.4%
862 369
 
0.4%
2475 369
 
0.4%
447 362
 
0.3%
236 351
 
0.3%
192 333
 
0.3%
399 332
 
0.3%
308 329
 
0.3%
Other values (3792) 100012
96.3%
ValueCountFrequency (%)
31 8
 
< 0.1%
56 8
 
< 0.1%
67 128
0.1%
73 59
0.1%
74 30
 
< 0.1%
76 1
 
< 0.1%
77 41
 
< 0.1%
78 30
 
< 0.1%
80 2
 
< 0.1%
82 7
 
< 0.1%
ValueCountFrequency (%)
4983 12
< 0.1%
4963 13
< 0.1%
4817 5
 
< 0.1%
4502 10
< 0.1%
4243 18
< 0.1%
4000 11
< 0.1%
3999 5
 
< 0.1%
3998 8
< 0.1%
3997 9
< 0.1%
3996 8
< 0.1%

Inflight wifi service
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7296832
Minimum0
Maximum5
Zeros3103
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size811.9 KiB
2024-10-17T15:42:13.228927image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3278295
Coefficient of variation (CV)0.48644088
Kurtosis-0.84616972
Mean2.7296832
Median Absolute Deviation (MAD)1
Skewness0.040408022
Sum283625
Variance1.7631311
MonotonicityNot monotonic
2024-10-17T15:42:13.348509image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 25868
24.9%
2 25830
24.9%
4 19794
19.1%
1 17840
17.2%
5 11469
11.0%
0 3103
 
3.0%
ValueCountFrequency (%)
0 3103
 
3.0%
1 17840
17.2%
2 25830
24.9%
3 25868
24.9%
4 19794
19.1%
5 11469
11.0%
ValueCountFrequency (%)
5 11469
11.0%
4 19794
19.1%
3 25868
24.9%
2 25830
24.9%
1 17840
17.2%
0 3103
 
3.0%

Departure/Arrival time convenient
Real number (ℝ)

ZEROS 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.060296
Minimum0
Maximum5
Zeros5300
Zeros (%)5.1%
Negative0
Negative (%)0.0%
Memory size811.9 KiB
2024-10-17T15:42:13.435065image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.5250752
Coefficient of variation (CV)0.49834237
Kurtosis-1.0377673
Mean3.060296
Median Absolute Deviation (MAD)1
Skewness-0.33439863
Sum317977
Variance2.3258544
MonotonicityNot monotonic
2024-10-17T15:42:13.527252image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 25546
24.6%
5 22403
21.6%
3 17966
17.3%
2 17191
16.5%
1 15498
14.9%
0 5300
 
5.1%
ValueCountFrequency (%)
0 5300
 
5.1%
1 15498
14.9%
2 17191
16.5%
3 17966
17.3%
4 25546
24.6%
5 22403
21.6%
ValueCountFrequency (%)
5 22403
21.6%
4 25546
24.6%
3 17966
17.3%
2 17191
16.5%
1 15498
14.9%
0 5300
 
5.1%

Ease of Online booking
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7569006
Minimum0
Maximum5
Zeros4487
Zeros (%)4.3%
Negative0
Negative (%)0.0%
Memory size811.9 KiB
2024-10-17T15:42:13.613389image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3989295
Coefficient of variation (CV)0.50742833
Kurtosis-0.91034621
Mean2.7569006
Median Absolute Deviation (MAD)1
Skewness-0.018294273
Sum286453
Variance1.9570037
MonotonicityNot monotonic
2024-10-17T15:42:13.704552image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 24449
23.5%
2 24021
23.1%
4 19571
18.8%
1 17525
16.9%
5 13851
13.3%
0 4487
 
4.3%
ValueCountFrequency (%)
0 4487
 
4.3%
1 17525
16.9%
2 24021
23.1%
3 24449
23.5%
4 19571
18.8%
5 13851
13.3%
ValueCountFrequency (%)
5 13851
13.3%
4 19571
18.8%
3 24449
23.5%
2 24021
23.1%
1 17525
16.9%
0 4487
 
4.3%

Gate location
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9768825
Minimum0
Maximum5
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size811.9 KiB
2024-10-17T15:42:13.792387image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.277621
Coefficient of variation (CV)0.42918087
Kurtosis-1.0302833
Mean2.9768825
Median Absolute Deviation (MAD)1
Skewness-0.058889412
Sum309310
Variance1.6323154
MonotonicityNot monotonic
2024-10-17T15:42:13.885761image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 28577
27.5%
4 24426
23.5%
2 19459
18.7%
1 17562
16.9%
5 13879
13.4%
0 1
 
< 0.1%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 17562
16.9%
2 19459
18.7%
3 28577
27.5%
4 24426
23.5%
5 13879
13.4%
ValueCountFrequency (%)
5 13879
13.4%
4 24426
23.5%
3 28577
27.5%
2 19459
18.7%
1 17562
16.9%
0 1
 
< 0.1%

Food and drink
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2021289
Minimum0
Maximum5
Zeros107
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size811.9 KiB
2024-10-17T15:42:13.975918image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3295327
Coefficient of variation (CV)0.41520275
Kurtosis-1.1454532
Mean3.2021289
Median Absolute Deviation (MAD)1
Skewness-0.1512795
Sum332714
Variance1.7676572
MonotonicityNot monotonic
2024-10-17T15:42:14.065811image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 24359
23.4%
5 22313
21.5%
3 22300
21.5%
2 21988
21.2%
1 12837
12.4%
0 107
 
0.1%
ValueCountFrequency (%)
0 107
 
0.1%
1 12837
12.4%
2 21988
21.2%
3 22300
21.5%
4 24359
23.4%
5 22313
21.5%
ValueCountFrequency (%)
5 22313
21.5%
4 24359
23.4%
3 22300
21.5%
2 21988
21.2%
1 12837
12.4%
0 107
 
0.1%

Online boarding
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2503753
Minimum0
Maximum5
Zeros2428
Zeros (%)2.3%
Negative0
Negative (%)0.0%
Memory size811.9 KiB
2024-10-17T15:42:14.153419image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.349509
Coefficient of variation (CV)0.41518557
Kurtosis-0.7020058
Mean3.2503753
Median Absolute Deviation (MAD)1
Skewness-0.4538517
Sum337727
Variance1.8211744
MonotonicityNot monotonic
2024-10-17T15:42:14.251646image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 30762
29.6%
3 21804
21.0%
5 20713
19.9%
2 17505
16.8%
1 10692
 
10.3%
0 2428
 
2.3%
ValueCountFrequency (%)
0 2428
 
2.3%
1 10692
 
10.3%
2 17505
16.8%
3 21804
21.0%
4 30762
29.6%
5 20713
19.9%
ValueCountFrequency (%)
5 20713
19.9%
4 30762
29.6%
3 21804
21.0%
2 17505
16.8%
1 10692
 
10.3%
0 2428
 
2.3%

Seat comfort
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.439396
Minimum0
Maximum5
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size811.9 KiB
2024-10-17T15:42:14.341021image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median4
Q35
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.3190875
Coefficient of variation (CV)0.38352302
Kurtosis-0.92570207
Mean3.439396
Median Absolute Deviation (MAD)1
Skewness-0.48277539
Sum357367
Variance1.7399919
MonotonicityNot monotonic
2024-10-17T15:42:14.432179image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 31765
30.6%
5 26470
25.5%
3 18696
18.0%
2 14897
14.3%
1 12075
 
11.6%
0 1
 
< 0.1%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 12075
 
11.6%
2 14897
14.3%
3 18696
18.0%
4 31765
30.6%
5 26470
25.5%
ValueCountFrequency (%)
5 26470
25.5%
4 31765
30.6%
3 18696
18.0%
2 14897
14.3%
1 12075
 
11.6%
0 1
 
< 0.1%

Inflight entertainment
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3581575
Minimum0
Maximum5
Zeros14
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size811.9 KiB
2024-10-17T15:42:14.518335image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median4
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3329907
Coefficient of variation (CV)0.39694109
Kurtosis-1.0606958
Mean3.3581575
Median Absolute Deviation (MAD)1
Skewness-0.36513059
Sum348926
Variance1.7768642
MonotonicityNot monotonic
2024-10-17T15:42:14.613072image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 29423
28.3%
5 25213
24.3%
3 19139
18.4%
2 17637
17.0%
1 12478
12.0%
0 14
 
< 0.1%
ValueCountFrequency (%)
0 14
 
< 0.1%
1 12478
12.0%
2 17637
17.0%
3 19139
18.4%
4 29423
28.3%
5 25213
24.3%
ValueCountFrequency (%)
5 25213
24.3%
4 29423
28.3%
3 19139
18.4%
2 17637
17.0%
1 12478
12.0%
0 14
 
< 0.1%

On-board service
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3823626
Minimum0
Maximum5
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size811.9 KiB
2024-10-17T15:42:14.701751image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median4
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.2883544
Coefficient of variation (CV)0.38090368
Kurtosis-0.89233524
Mean3.3823626
Median Absolute Deviation (MAD)1
Skewness-0.42003075
Sum351441
Variance1.659857
MonotonicityNot monotonic
2024-10-17T15:42:14.793424image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 30867
29.7%
5 23648
22.8%
3 22833
22.0%
2 14681
14.1%
1 11872
 
11.4%
0 3
 
< 0.1%
ValueCountFrequency (%)
0 3
 
< 0.1%
1 11872
 
11.4%
2 14681
14.1%
3 22833
22.0%
4 30867
29.7%
5 23648
22.8%
ValueCountFrequency (%)
5 23648
22.8%
4 30867
29.7%
3 22833
22.0%
2 14681
14.1%
1 11872
 
11.4%
0 3
 
< 0.1%

Leg room service
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3510548
Minimum0
Maximum5
Zeros472
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size811.9 KiB
2024-10-17T15:42:14.881690image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median4
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3156046
Coefficient of variation (CV)0.39259418
Kurtosis-0.98025691
Mean3.3510548
Median Absolute Deviation (MAD)1
Skewness-0.35023134
Sum348188
Variance1.7308155
MonotonicityNot monotonic
2024-10-17T15:42:14.975106image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 28789
27.7%
5 24667
23.7%
3 20098
19.3%
2 19525
18.8%
1 10353
 
10.0%
0 472
 
0.5%
ValueCountFrequency (%)
0 472
 
0.5%
1 10353
 
10.0%
2 19525
18.8%
3 20098
19.3%
4 28789
27.7%
5 24667
23.7%
ValueCountFrequency (%)
5 24667
23.7%
4 28789
27.7%
3 20098
19.3%
2 19525
18.8%
1 10353
 
10.0%
0 472
 
0.5%

Baggage handling
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size811.9 KiB
4
37383 
5
27131 
3
20632 
2
11521 
1
7237 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters103904
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row3
3rd row4
4th row3
5th row4

Common Values

ValueCountFrequency (%)
4 37383
36.0%
5 27131
26.1%
3 20632
19.9%
2 11521
 
11.1%
1 7237
 
7.0%

Length

2024-10-17T15:42:15.077127image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-17T15:42:15.172831image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
4 37383
36.0%
5 27131
26.1%
3 20632
19.9%
2 11521
 
11.1%
1 7237
 
7.0%

Most occurring characters

ValueCountFrequency (%)
4 37383
36.0%
5 27131
26.1%
3 20632
19.9%
2 11521
 
11.1%
1 7237
 
7.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 103904
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 37383
36.0%
5 27131
26.1%
3 20632
19.9%
2 11521
 
11.1%
1 7237
 
7.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 103904
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 37383
36.0%
5 27131
26.1%
3 20632
19.9%
2 11521
 
11.1%
1 7237
 
7.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 103904
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 37383
36.0%
5 27131
26.1%
3 20632
19.9%
2 11521
 
11.1%
1 7237
 
7.0%

Checkin service
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3042905
Minimum0
Maximum5
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size811.9 KiB
2024-10-17T15:42:15.273081image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.2653958
Coefficient of variation (CV)0.38295538
Kurtosis-0.82887706
Mean3.3042905
Median Absolute Deviation (MAD)1
Skewness-0.36498196
Sum343329
Variance1.6012266
MonotonicityNot monotonic
2024-10-17T15:42:15.365813image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 29055
28.0%
3 28446
27.4%
5 20619
19.8%
2 12893
12.4%
1 12890
12.4%
0 1
 
< 0.1%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 12890
12.4%
2 12893
12.4%
3 28446
27.4%
4 29055
28.0%
5 20619
19.8%
ValueCountFrequency (%)
5 20619
19.8%
4 29055
28.0%
3 28446
27.4%
2 12893
12.4%
1 12890
12.4%
0 1
 
< 0.1%

Inflight service
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6404277
Minimum0
Maximum5
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size811.9 KiB
2024-10-17T15:42:15.456570image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median4
Q35
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.175663
Coefficient of variation (CV)0.3229464
Kurtosis-0.3575092
Mean3.6404277
Median Absolute Deviation (MAD)1
Skewness-0.69031396
Sum378255
Variance1.3821836
MonotonicityNot monotonic
2024-10-17T15:42:15.549793image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 37945
36.5%
5 27116
26.1%
3 20299
19.5%
2 11457
 
11.0%
1 7084
 
6.8%
0 3
 
< 0.1%
ValueCountFrequency (%)
0 3
 
< 0.1%
1 7084
 
6.8%
2 11457
 
11.0%
3 20299
19.5%
4 37945
36.5%
5 27116
26.1%
ValueCountFrequency (%)
5 27116
26.1%
4 37945
36.5%
3 20299
19.5%
2 11457
 
11.0%
1 7084
 
6.8%
0 3
 
< 0.1%

Cleanliness
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2863509
Minimum0
Maximum5
Zeros12
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size811.9 KiB
2024-10-17T15:42:15.639781image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3122728
Coefficient of variation (CV)0.39931003
Kurtosis-1.0125577
Mean3.2863509
Median Absolute Deviation (MAD)1
Skewness-0.30007449
Sum341465
Variance1.72206
MonotonicityNot monotonic
2024-10-17T15:42:15.734476image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 27179
26.2%
3 24574
23.7%
5 22689
21.8%
2 16132
15.5%
1 13318
12.8%
0 12
 
< 0.1%
ValueCountFrequency (%)
0 12
 
< 0.1%
1 13318
12.8%
2 16132
15.5%
3 24574
23.7%
4 27179
26.2%
5 22689
21.8%
ValueCountFrequency (%)
5 22689
21.8%
4 27179
26.2%
3 24574
23.7%
2 16132
15.5%
1 13318
12.8%
0 12
 
< 0.1%

Departure Delay in Minutes
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct446
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.815618
Minimum0
Maximum1592
Zeros58668
Zeros (%)56.5%
Negative0
Negative (%)0.0%
Memory size811.9 KiB
2024-10-17T15:42:15.843751image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q312
95-th percentile78
Maximum1592
Range1592
Interquartile range (IQR)12

Descriptive statistics

Standard deviation38.230901
Coefficient of variation (CV)2.5804458
Kurtosis100.26701
Mean14.815618
Median Absolute Deviation (MAD)0
Skewness6.7339795
Sum1539402
Variance1461.6018
MonotonicityNot monotonic
2024-10-17T15:42:15.979869image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 58668
56.5%
1 2948
 
2.8%
2 2274
 
2.2%
3 2009
 
1.9%
4 1854
 
1.8%
5 1692
 
1.6%
6 1517
 
1.5%
7 1392
 
1.3%
8 1295
 
1.2%
9 1255
 
1.2%
Other values (436) 29000
27.9%
ValueCountFrequency (%)
0 58668
56.5%
1 2948
 
2.8%
2 2274
 
2.2%
3 2009
 
1.9%
4 1854
 
1.8%
5 1692
 
1.6%
6 1517
 
1.5%
7 1392
 
1.3%
8 1295
 
1.2%
9 1255
 
1.2%
ValueCountFrequency (%)
1592 1
< 0.1%
1305 1
< 0.1%
1017 1
< 0.1%
978 1
< 0.1%
933 1
< 0.1%
930 1
< 0.1%
921 1
< 0.1%
859 1
< 0.1%
853 1
< 0.1%
750 1
< 0.1%

Arrival Delay in Minutes
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct455
Distinct (%)0.4%
Missing310
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean15.178678
Minimum0
Maximum1584
Zeros58159
Zeros (%)56.0%
Negative0
Negative (%)0.0%
Memory size811.9 KiB
2024-10-17T15:42:16.107936image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q313
95-th percentile79
Maximum1584
Range1584
Interquartile range (IQR)13

Descriptive statistics

Standard deviation38.698682
Coefficient of variation (CV)2.5495423
Kurtosis94.537006
Mean15.178678
Median Absolute Deviation (MAD)0
Skewness6.5966368
Sum1572420
Variance1497.588
MonotonicityNot monotonic
2024-10-17T15:42:16.241312image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 58159
56.0%
1 2211
 
2.1%
2 2064
 
2.0%
3 1952
 
1.9%
4 1907
 
1.8%
5 1658
 
1.6%
6 1616
 
1.6%
7 1481
 
1.4%
8 1394
 
1.3%
9 1264
 
1.2%
Other values (445) 29888
28.8%
ValueCountFrequency (%)
0 58159
56.0%
1 2211
 
2.1%
2 2064
 
2.0%
3 1952
 
1.9%
4 1907
 
1.8%
5 1658
 
1.6%
6 1616
 
1.6%
7 1481
 
1.4%
8 1394
 
1.3%
9 1264
 
1.2%
ValueCountFrequency (%)
1584 1
< 0.1%
1280 1
< 0.1%
1011 1
< 0.1%
970 1
< 0.1%
952 1
< 0.1%
924 1
< 0.1%
920 1
< 0.1%
860 1
< 0.1%
823 1
< 0.1%
729 1
< 0.1%

satisfaction
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size811.9 KiB
neutral or dissatisfied
58879 
satisfied
45025 

Length

Max length23
Median length23
Mean length16.933342
Min length9

Characters and Unicode

Total characters1759442
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowneutral or dissatisfied
2nd rowneutral or dissatisfied
3rd rowsatisfied
4th rowneutral or dissatisfied
5th rowsatisfied

Common Values

ValueCountFrequency (%)
neutral or dissatisfied 58879
56.7%
satisfied 45025
43.3%

Length

2024-10-17T15:42:16.368739image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-17T15:42:16.466748image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
neutral 58879
26.6%
or 58879
26.6%
dissatisfied 58879
26.6%
satisfied 45025
20.3%

Most occurring characters

ValueCountFrequency (%)
i 266687
15.2%
s 266687
15.2%
e 162783
9.3%
t 162783
9.3%
a 162783
9.3%
d 162783
9.3%
r 117758
6.7%
117758
6.7%
f 103904
 
5.9%
n 58879
 
3.3%
Other values (3) 176637
10.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1759442
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 266687
15.2%
s 266687
15.2%
e 162783
9.3%
t 162783
9.3%
a 162783
9.3%
d 162783
9.3%
r 117758
6.7%
117758
6.7%
f 103904
 
5.9%
n 58879
 
3.3%
Other values (3) 176637
10.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1759442
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 266687
15.2%
s 266687
15.2%
e 162783
9.3%
t 162783
9.3%
a 162783
9.3%
d 162783
9.3%
r 117758
6.7%
117758
6.7%
f 103904
 
5.9%
n 58879
 
3.3%
Other values (3) 176637
10.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1759442
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 266687
15.2%
s 266687
15.2%
e 162783
9.3%
t 162783
9.3%
a 162783
9.3%
d 162783
9.3%
r 117758
6.7%
117758
6.7%
f 103904
 
5.9%
n 58879
 
3.3%
Other values (3) 176637
10.0%

Interactions

2024-10-17T15:42:08.348352image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:41:35.170912image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:41:37.101360image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:41:38.945549image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:41:40.911963image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:41:42.679514image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:41:44.609856image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:41:46.385745image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:41:48.152452image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:41:50.057704image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:41:51.794136image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:41:53.693020image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:41:55.485543image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:41:57.212970image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:41:59.129170image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:42:00.880400image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:42:02.680479image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:42:04.540447image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:42:06.314028image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:42:08.445548image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:41:35.305870image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:41:37.197037image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:41:39.040780image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:41:41.003207image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:41:42.789243image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:41:44.701979image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:41:46.479375image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:41:48.240610image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:41:50.152976image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:41:51.885343image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:41:53.783431image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:41:55.575236image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:41:57.312213image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:41:59.218924image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:42:00.971758image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:42:02.770589image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:42:04.630262image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:42:06.415963image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:42:08.543765image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:41:35.479242image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:41:37.289192image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:41:39.139549image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:41:41.095974image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:41:42.889251image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:41:44.798698image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:41:46.572549image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:41:48.332222image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:41:50.246809image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:41:51.978035image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:41:53.877127image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:41:55.667412image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:41:57.408732image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:41:59.312096image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:42:01.066740image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:42:02.862726image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:42:04.724663image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:42:06.517649image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:42:08.650047image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:41:35.580062image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:41:37.393879image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:41:39.240505image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:41:41.191260image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:41:42.990214image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:41:44.897218image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:41:46.671252image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:41:48.424888image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:41:50.344107image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:41:52.071228image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:41:53.977071image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:41:55.763558image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:41:57.503477image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:41:59.408468image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:42:01.162957image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:42:02.957268image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:42:04.819934image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:42:06.620124image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:42:08.746754image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:41:35.680198image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:41:37.493150image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:41:39.337169image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:41:41.282612image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:41:43.081291image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:41:44.992404image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:41:46.762891image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:41:48.516229image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:41:50.438067image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:41:52.161445image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:41:54.071234image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:41:55.853252image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:41:57.595134image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:41:59.496654image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:42:01.254771image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:42:03.046916image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:42:04.910424image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:42:06.720354image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:42:08.844790image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:41:35.781262image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:41:37.589463image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:41:39.432997image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:41:41.373763image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:41:43.171985image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:41:45.083213image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:41:46.854821image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:41:48.602684image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:41:50.528043image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:41:52.255517image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:41:54.161373image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:41:55.942048image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:41:57.685788image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:41:59.597366image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:42:01.342762image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:42:03.133962image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:42:05.003620image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:42:06.819564image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:42:08.945441image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:41:35.882402image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:41:37.695187image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:41:39.532228image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:41:41.465947image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:41:43.271275image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:41:45.172414image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:41:46.946924image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:41:48.692246image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:41:50.623140image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:41:52.345245image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:41:54.280450image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:41:56.033290image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:41:57.780809image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:41:59.687034image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:42:01.439642image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:42:03.354228image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:42:05.108416image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:42:06.916757image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:42:09.044912image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:41:35.975339image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:41:37.789512image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:41:39.626914image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:41:41.554132image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:41:43.372131image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:41:45.264194image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:41:47.039585image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:41:48.779457image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:41:50.714670image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:41:52.439581image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:41:54.375622image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:41:56.121356image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2024-10-17T15:41:40.804758image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:41:42.574727image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:41:44.508980image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:41:46.282476image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:41:48.050711image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:41:49.957670image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:41:51.691825image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:41:53.469960image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:41:55.385850image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:41:57.111609image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:41:59.027974image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:42:00.778875image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:42:02.582398image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:42:04.437689image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:42:06.213380image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-17T15:42:08.238017image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Correlations

2024-10-17T15:42:16.689888image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
AgeArrival Delay in MinutesBaggage handlingCheckin serviceClassCleanlinessCustomer TypeDeparture Delay in MinutesDeparture/Arrival time convenientEase of Online bookingFlight DistanceFood and drinkGate locationGenderInflight entertainmentInflight serviceInflight wifi serviceLeg room serviceOn-board serviceOnline boardingSeat comfortType of TravelUnnamed: 0idsatisfaction
Age1.000-0.0120.0610.0400.2070.0540.375-0.0110.0360.0230.0720.021-0.0020.0140.082-0.0310.0170.0520.0700.2160.1610.3400.0050.0260.284
Arrival Delay in Minutes-0.0121.0000.006-0.0350.000-0.0310.0000.741-0.006-0.014-0.001-0.0330.0050.008-0.044-0.055-0.037-0.020-0.048-0.050-0.0370.000-0.004-0.0050.017
Baggage handling0.0610.0061.0000.1420.1350.0630.0670.0070.0720.0320.0370.0360.0540.0480.3520.4910.1200.2670.3990.0930.0820.0510.0000.0640.288
Checkin service0.040-0.0350.1421.0000.1260.1750.033-0.0180.1000.0110.0720.084-0.0360.0110.1210.2500.0430.1450.2350.2180.1990.019-0.0040.0750.249
Class0.2070.0000.1350.1261.0000.1150.1230.0000.1000.1160.3440.0770.1110.0120.1520.1310.1020.1630.1600.2490.1750.5540.0000.1290.505
Cleanliness0.054-0.0310.0630.1750.1151.0000.105-0.0160.0140.0150.0810.647-0.0040.0180.6810.1010.1310.0970.1250.3460.6670.093-0.0020.0230.314
Customer Type0.3750.0000.0670.0330.1230.1051.0000.0000.2930.0550.2480.0790.1250.0320.1200.0550.0370.0760.0770.1950.1730.3080.0060.0140.188
Departure Delay in Minutes-0.0110.7410.007-0.0180.000-0.0160.0001.000-0.003-0.0110.027-0.0210.0040.006-0.027-0.032-0.030-0.006-0.027-0.033-0.0200.000-0.0060.0570.017
Departure/Arrival time convenient0.036-0.0060.0720.1000.1000.0140.293-0.0031.0000.440-0.0130.0030.4500.009-0.0090.0910.3390.0070.0720.0620.0120.2900.000-0.0030.066
Ease of Online booking0.023-0.0140.0320.0110.1160.0150.055-0.0110.4401.0000.0660.0300.4620.0060.0430.0350.7120.0950.0370.3680.0270.1890.0020.0120.316
Flight Distance0.072-0.0010.0370.0720.3440.0810.2480.027-0.0130.0661.0000.0470.0010.0100.1050.0600.0060.1180.1000.1940.1370.2810.0030.1330.312
Food and drink0.021-0.0330.0360.0840.0770.6470.079-0.0210.0030.0300.0471.000-0.0010.0100.6100.0440.1330.0310.0580.2410.5580.076-0.003-0.0010.224
Gate location-0.0020.0050.054-0.0360.111-0.0040.1250.0040.4500.4620.001-0.0011.0000.0070.003-0.0070.333-0.006-0.028-0.0010.0020.1550.004-0.0010.155
Gender0.0140.0080.0480.0110.0120.0180.0320.0060.0090.0060.0100.0100.0071.0000.0060.0460.0080.0540.0220.0440.0350.0060.0000.0070.012
Inflight entertainment0.082-0.0440.3520.1210.1520.6810.120-0.027-0.0090.0430.1050.6100.0030.0061.0000.4220.2000.3140.4370.3020.6040.1650.0010.0020.422
Inflight service-0.031-0.0550.4910.2500.1310.1010.055-0.0320.0910.0350.0600.044-0.0070.0460.4221.0000.1050.3730.5690.1090.0980.041-0.0000.0740.282
Inflight wifi service0.017-0.0370.1200.0430.1020.1310.037-0.0300.3390.7120.0060.1330.3330.0080.2000.1051.0000.1500.1170.4360.1190.183-0.003-0.0230.525
Leg room service0.052-0.0200.2670.1450.1630.0970.076-0.0060.0070.0950.1180.031-0.0060.0540.3140.3730.1501.0000.3640.1390.1200.1710.0050.0420.344
On-board service0.070-0.0480.3990.2350.1600.1250.077-0.0270.0720.0370.1000.058-0.0280.0220.4370.5690.1170.3641.0000.1760.1470.0870.0010.0520.333
Online boarding0.216-0.0500.0930.2180.2490.3460.195-0.0330.0620.3680.1940.241-0.0010.0440.3020.1090.4360.1390.1761.0000.4400.2380.0010.0560.618
Seat comfort0.161-0.0370.0820.1990.1750.6670.173-0.0200.0120.0270.1370.5580.0020.0350.6040.0980.1190.1200.1470.4401.0000.133-0.0010.0540.389
Type of Travel0.3400.0000.0510.0190.5540.0930.3080.0000.2900.1890.2810.0760.1550.0060.1650.0410.1830.1710.0870.2380.1331.0000.0000.0190.449
Unnamed: 00.005-0.0040.000-0.0040.000-0.0020.006-0.0060.0000.0020.003-0.0030.0040.0000.001-0.000-0.0030.0050.0010.001-0.0010.0001.0000.0030.006
id0.026-0.0050.0640.0750.1290.0230.0140.057-0.0030.0120.133-0.001-0.0010.0070.0020.074-0.0230.0420.0520.0560.0540.0190.0031.0000.025
satisfaction0.2840.0170.2880.2490.5050.3140.1880.0170.0660.3160.3120.2240.1550.0120.4220.2820.5250.3440.3330.6180.3890.4490.0060.0251.000

Missing values

2024-10-17T15:42:10.397582image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-10-17T15:42:10.822913image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Unnamed: 0idGenderCustomer TypeAgeType of TravelClassFlight DistanceInflight wifi serviceDeparture/Arrival time convenientEase of Online bookingGate locationFood and drinkOnline boardingSeat comfortInflight entertainmentOn-board serviceLeg room serviceBaggage handlingCheckin serviceInflight serviceCleanlinessDeparture Delay in MinutesArrival Delay in Minutessatisfaction
0070172MaleLoyal Customer13Personal TravelEco Plus460343153554344552518.0neutral or dissatisfied
115047Maledisloyal Customer25Business travelBusiness2353233131115314116.0neutral or dissatisfied
22110028FemaleLoyal Customer26Business travelBusiness11422222555543444500.0satisfied
3324026FemaleLoyal Customer25Business travelBusiness56225552222253142119.0neutral or dissatisfied
44119299MaleLoyal Customer61Business travelBusiness2143333455334433300.0satisfied
55111157FemaleLoyal Customer26Personal TravelEco11803421121134444100.0neutral or dissatisfied
6682113MaleLoyal Customer47Personal TravelEco127624232222334352923.0neutral or dissatisfied
7796462FemaleLoyal Customer52Business travelBusiness20354344555555545440.0satisfied
8879485FemaleLoyal Customer41Business travelBusiness8531222433112141200.0neutral or dissatisfied
9965725Maledisloyal Customer20Business travelEco10613334233223443200.0neutral or dissatisfied
Unnamed: 0idGenderCustomer TypeAgeType of TravelClassFlight DistanceInflight wifi serviceDeparture/Arrival time convenientEase of Online bookingGate locationFood and drinkOnline boardingSeat comfortInflight entertainmentOn-board serviceLeg room serviceBaggage handlingCheckin serviceInflight serviceCleanlinessDeparture Delay in MinutesArrival Delay in Minutessatisfaction
10389410389486549MaleLoyal Customer26Business travelBusiness712444455553443451726.0satisfied
10389510389566030Femaledisloyal Customer24Business travelEco1055111211113355411310.0neutral or dissatisfied
10389610389671445MaleLoyal Customer57Business travelEco8674555444434313400.0neutral or dissatisfied
103897103897102203FemaleLoyal Customer60Business travelBusiness15995555554444444497.0satisfied
10389810389860666MaleLoyal Customer50Personal TravelEco16203134232243424200.0neutral or dissatisfied
10389910389994171Femaledisloyal Customer23Business travelEco1922123222231423230.0neutral or dissatisfied
10390010390073097MaleLoyal Customer49Business travelBusiness23474444245555555400.0satisfied
10390110390168825Maledisloyal Customer30Business travelBusiness199511134154324554714.0neutral or dissatisfied
10390210390254173Femaledisloyal Customer22Business travelEco10001115111145154100.0neutral or dissatisfied
10390310390362567MaleLoyal Customer27Business travelBusiness17231333111111443100.0neutral or dissatisfied